Untapped Potential of Unobtrusive Observation for Studying Health Behaviors

JMIR PUBLIC HEALTH AND SURVEILLANCE(2024)

引用 0|浏览1
暂无评分
摘要
Improving the environment is an important upstream intervention to promote population health by influencing health behaviors such as physical activity, smoking, and social distancing. Examples of promising environmental interventions include creating high-quality green spaces, building active transport infrastructure, and implementing urban planning regulations. However, there is little robust evidence to inform policy and decision makers about what kinds of environmental interventions are effective and for which populations. In this viewpoint, we make the case that this evidence gap exists partly because health behavior research is dominated by obtrusive methods that focus on studying individual behavior and that are less suitable for understanding environmental influences. In contrast, unobtrusive observation can assess how behavior varies in different environmental contexts. It thereby provides valuable data relating to how environments affect the behavior of populations, which is often useful knowledge for effectively and equitably tackling population health challenges such as obesity and noncommunicable diseases. Yet despite a long history, unobtrusive observation methods are currently underused in health behavior research. We discuss how developing the use of video technology and automated computer vision techniques can offer a scalable solution for assessing health behaviors, facilitating a more thorough investigation of how environments influence health behaviors. We also reflect on the important ethical challenges associated with unobtrusive observation and the use of these emerging video technologies. By increasing the use of unobtrusive observation alongside other methods, we strongly believe this will improve our understanding of the influences of the environment on health behaviors.
更多
查看译文
关键词
health behavior,environments,context,unobtrusive observation,video technology,computer vision
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要